# The package for animating ggplot2
library(gganimate); library(gapminder)
# the ggplot code used to plot the data
ggplot(
data = gapminder %>% filter(continent != "Oceania"),
aes(gdpPercap, lifeExp, size = pop, color = country)
) +
geom_point(alpha = 0.7, show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
scale_x_log10("GDP per capita", label = scales::comma) +
facet_wrap(~continent) +
# theme_pander(base_size = 16) +
theme(panel.border = element_rect(color = "grey90", fill = NA)) +
# Here comes the gganimate-specific bits
labs(title = "Year: {frame_time}") +
ylab("Life Expectancy") +
transition_time(year) +
ease_aes("linear")Introduction and Overview
EC320, Lecture 1
Dante Yasui
06 2024
Prologue
Motivation
What is the goal of econometrics?
To learn about the world using data.
Why do economists (and others) study econometrics?
Providing answers to important problems. Gaining confidence in our understanding of the world.
Ex.
- Do minimum wage policies reduce poverty?
- Does the death penalty deter violent crime?
- What are the harms of toxic pollutants like lead?
- What explains the gender pay gap?
- Can we forecast the next recession?
- Who gains and who loses from globalization?
Motivation
What is the goal of econometrics?
To learn about the world using data.
Why do economists (and others) study econometrics?
Providing answers to important problems.
How do you pronounce it?
Motivation
Why should you study econometrics?
Develop skills and learn to use tools that are valued by employers.
Cultivate a healthy sense of skepticism
Develop and feed your own curiosity about the world.
IMO1, of all the courses in a typical economics major, econometrics is the most translatable to a job
- Data is the new oil
- Extracting meaningful analysis from big data is a sought after skill in the job market of 2024
Motivation
Why should you study econometrics?
Throughout this course, I will try my best to emphasize why:
- Why are we learning this?
- Why does this matter with regard to future econometrics courses?
- Why is fill in the blank important for answering important problems?
- Why does this matter to employers?
Econometrics is built on crucial fundamentals. These fundamentals is the focus of this class.
uk · kaa · nuh · meh · truhks
Most econometric inquiry concerns one of two distinct goals:
- Prediction: Accurately predict or forecast an outcome given a set of predictors. Given what we know about \(x\), what values do we expect \(y\) to take?
- Causal identification: Estimate the effect of an intervention on an outcome. How does \(y\) change when we change \(x\)?
In this class, and in EC 421, we will focus on the later. The former is the focus of EC 422 and EC 524
In EC320
We start to build up the fundamentals of causal analysis
But first we need to build up the necessary Theory, Tools, and Skills
This course will focus almost exclusively on a particular method that is common in statistics in general:
- Ordinary Least Squares (OLS) (aka linear regression)
Causal Identification
Causal identification
Common refrain.1
“Correlation does not necessarily imply causation.”
Why might correlation fail to describe a causal relationship?
- Omitted-variables bias
- Selection bias
- Simultaneity
- Reverse causality
- Coincidence
Causal identification
Common refrain.1
“Correlation does not necessarily imply causation.”
Correlation may imply causation if we assume “all else equals”
- Hold everything fixed
This assumption is fragile in the real world.
Solutions:
- Conduct experiments
- Find a natural experiment
Do you think this is a causal statement?
Experiments
How can we ensure the all else equals assumption holds?
Randomization
Randomized Controlled Trails (RCT)
- widely used across many scientific disciplines1
- often touted as the gold standard of causal identification
- use randomization to ensure all else equals
In 2019, the Nobel Prize winners adapting RCTs to projects in development economics2
Experiments Ex.
Research question
Does health insurance improve health?
The all else equals assumption would require:
- all preexisting correlates with health must be the same across insured and uninsured
What would violate this assumption?
If more money is correlated with better health, and the average income of those who buy health insurance is higher, then we violate this assumption
Experiments Ex.
But what if health insurance is randomly assigned?
- Then, assuming the assignment is perfectly random across a large enough sample size, this assumption becomes much more palatable
Oregon Health Insurance Experiment
The Oregon Health Insurance Experiment is a landmark study of the effect of expanding public health insurance on health care use, health outcomes, financial strain, and well-being of low-income adults… In 2008, the state of Oregon drew names by lottery for its Medicaid program for low-income, uninsured adults, generating just such an opportunity. This ongoing analysis represents a collaborative effort between researchers and the state of Oregon to learn about the costs and benefits of expanding public health insurance.
Natural experiments
An external, non-experimental factor creates circumstances that resemble a controlled experiment
Real-world events provide opportunity to compare similar groups
With some assumptions, researchers infer the causal relationships examining differences in outcomes between groups
Natural experiments
Any examples of natural experiments that come to mind?
Here are some of the more famous ones:
- Vietnam draft lottery
- The Mariel Boatlift
- Maimonides’ Rule and Class Size
- The Opening of the London Congestion Charge
In more recent news:
EC320
Syllabus
Coursework
Rough outline of topics:
- 01: Introduction and review
- 02: The econometric problem
- 03: SLR estimation
- 04: SLR assumptions
- 05: SLR inference
- Midterm after 2nd week
- 06: MLR estimation
- 07: MLR inference
- 08: Transformations
- 09: Qualitative variables
- 10: Heteroskedasticity
- Final after 4th week
Final Due: Sunday, July 21st @ 08:00a
Course site
I use GitHub to host a separate site with all the course materials
You can find a link to it here or on the Canvas homepage
I use it because:
- I am adapting from previous class github repo
- it is convenient to track changes and keep everything up to date
- acts as a secondary site in case Canvas dies
I will try my best to keep Canvas up to date, but if you can’t find something there check the github.
All video recordings will only be available on Canvas
About me
Please call me Dante
- Office hours:
- Tuesdays: 10-11am PST
- Thursdays: 3-4pm
- Lab:
- Wednesdays: noon-2pm
- Email: dyasui@uoregon.edu
> Metrics
- I ❤️ studying econometrics
- My first time teaching EC320 but I TAed for this class twice
- Instructor: EC327 Winter 2024
About me
Please call me Dante
- Office hours:
- Tuesdays: 10-11am PST
- Thursdays: 3-4pm
- Lab:
- Wednesdays: noon-2pm
- Email: dyasui@uoregon.edu
> Grad school
- 3rd year Econ PhD student
- Applied topics related to migration and historical data
- Causal inference, statistical learning, and data science
About me
Please call me Dante
> Before grad school
- Grew up in Davis, CA
- Traveled around CA for gymnastics and quizbowl competitions in high school
- Studied Economics and Japanese at college in Portland, OR
- Prior to PhD, researched economic history and geography
In EC320
An applied econometrician1 needs a solid grasp on (at least) three areas:
- The underlying theory (assumptions, strengths, weaknesses).
- An ability to load, aggregating, joining, visualizing large datasets.
- Applying the theoretical methods to actual data.
This course aims to deepen your knowledge in each of these three areas.
- 1: Analytical skills (Math)
- 2-3: Computational tools ()
What is ?
To quote the project website1
R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.
What does that mean?
was created for the statistical and graphical work required by econometrics –written by statistical programmers
has a vibrant, thriving online community. (stack overflow)
Plus it’s free and open source
Why are we using ?
1. is free and open source —saving both you and the university money.
2. Related: Outside of a small group of economists, private- and public-sector employers favor over Stata and most competing softwares.
3. is very flexible and powerful —adaptable to nearly any task, e.g., ’metrics, spatial data analysis, machine learning, web scraping, data cleaning, website building, teaching. I write all my slides, problem sets, and exams in R.
Why are we using ?
4. Related: imposes no artificial restrictions on your amount of observations, variables, memory, or processing power.
5. If you put in the work, 1 you will come away with a valuable and marketable tool.
6. Learning R and the tidyverse will help you to learn other useful tools like SQL, python, etc.
Cool examples of what you can do with
